A Modified Fuzzy C-Means Algorithm for Association Rules Clustering
The Fuzzy C-Means (FCM) algorithm is commonly used for clustering. It is one of the problems in association rules mining that a great number of rules generated from the dataset makes it difficult to analyze and use. From the angle of knowledge management, a modified FCM algorithm is proposed and applied to association rules clustering, which partitions these rules into the given classes by the attribute’s weight based on information gain for evaluating the attribute’s importance. Experiment with the UCI dataset shows that this algorithm can efficiently cluster the association rules for a user to understand.
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